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    "# Weighted Belief Propagation Decoding\n",
    "\n",
    "This notebooks implements the *Weighted Belief Propagation* (BP) algorithm as proposed by Nachmani *et al.* in [1].\n",
    "The main idea is to leverage BP decoding by additional trainable weights that scale each outgoing variable node (VN) and check node (CN) message. These weights provide additional degrees of freedom and can be trained by stochastic gradient descent (SGD) to improve the BP performance for the given code. If all weights are initialized with *1*, the algorithm equals the *classical* BP algorithm and, thus, the concept can be seen as a generalized BP decoder.\n",
    "\n",
    "Our main focus is to show how Sionna can lower the barrier-to-entry for state-of-the-art research.\n",
    "For this, you will investigate:\n",
    "\n",
    "* How to implement the multi-loss BP decoding with Sionna\n",
    "* How a single scaling factor can lead to similar results\n",
    "* What happens for training of the 5G LDPC code\n",
    "\n",
    "The setup includes the following components:\n",
    "\n",
    "- LDPC BP Decoder\n",
    "- Gaussian LLR source\n",
    "\n",
    "Please note that we implement a simplified version of the original algorithm consisting of two major simplifications:\n",
    "\n",
    "1. ) Only outgoing variable node (VN) messages are weighted. This is possible as the VN operation is linear and it would only increase the memory complexity without increasing the *expressive* power of the neural network.\n",
    "\n",
    "2. ) We use the same shared weights for all iterations. This can potentially influence the final performance, however, simplifies the implementation and allows to run the decoder with different number of iterations.\n",
    "\n",
    "\n",
    "**Note**: If you are not familiar with all-zero codeword-based simulations please have a look into the [Bit-Interleaved Coded Modulation](https://nvlabs.github.io/sionna/phy/tutorials/Bit_Interleaved_Coded_Modulation.html) example notebook first.\n",
    "\n",
    "## Table of Contents\n",
    "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n",
    "* [Weighted BP for BCH Codes](#Weighted-BP-for-BCH-Codes)\n",
    "    * [Weights *before* Training and Simulation of BER](#Weights-before-Training-and-Simulation-of-BER)\n",
    "    * [Training](#Training)\n",
    "    * [Results](#Results)\n",
    "* [Further Experiments](#Further-Experiments)\n",
    "    * [Damped BP](#Damped-BP)\n",
    "    * [Learning the 5G LDPC Code](#Learning-the-5G-LDPC-Code)\n",
    "* [References](#References)"
   ]
  },
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   "source": [
    "![System Model]()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GPU Configuration and Imports"
   ]
  },
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   "source": [
    "import os\n",
    "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n",
    "    gpu_num = 0 # Use \"\" to use the CPU\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna.phy\n",
    "except ImportError as e:\n",
    "    import sys\n",
    "    if 'google.colab' in sys.modules:\n",
    "       # Install Sionna in Google Colab\n",
    "       print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
    "       os.system(\"pip install sionna\")\n",
    "       os.kill(os.getpid(), 5)\n",
    "    else:\n",
    "       raise e\n",
    "\n",
    "import tensorflow as tf\n",
    "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n",
    "# For more details, see https://www.tensorflow.org/guide/gpu\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        tf.config.experimental.set_memory_growth(gpus[0], True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "# Avoid warnings from TensorFlow\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "\n",
    "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation\n",
    "\n",
    "# Import required Sionna components\n",
    "from sionna.phy.fec.ldpc import LDPCBPDecoder, LDPC5GEncoder, LDPC5GDecoder, WeightedBPCallback\n",
    "from sionna.phy.fec.utils import GaussianPriorSource, load_parity_check_examples, llr2mi\n",
    "from sionna.phy.utils import ebnodb2no, hard_decisions\n",
    "from sionna.phy.utils.metrics import compute_ber\n",
    "from sionna.phy.utils.plotting import PlotBER\n",
    "from tensorflow.keras.losses import BinaryCrossentropy\n",
    "from sionna.phy import Block\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
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   "source": [
    "## Weighted BP for BCH Codes"
   ]
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    "First, we define the trainable model consisting of:\n",
    "\n",
    "- LDPC BP decoder\n",
    "- Gaussian LLR source\n",
    "\n",
    "The idea of the multi-loss function in [1] is to average the loss overall iterations, i.e., not just the final estimate is evaluated. This requires to call the BP decoder *iteration-wise* by setting `num_iter=1` and `return_state=True` such that the decoder will perform a single iteration and returns its current estimate while also providing the internal messages for the next iteration.\n",
    "\n",
    "A few comments:\n",
    "\n",
    "- We assume the transmission of the all-zero codeword. This allows to train and analyze the decoder without the need of an encoder. Remark: The final decoder can be used for arbitrary codewords.\n",
    "- We directly generate the channel LLRs with `GaussianPriorSource`. The equivalent LLR distribution could be achieved by transmitting the all-zero codeword over an AWGN channel with BPSK modulation.\n",
    "- For the proposed *multi-loss* [1] (i.e., the loss is averaged over all iterations), we need to access the decoders intermediate output after each iteration. This is done by calling the decoding function multiple times while setting `return_state` to True, i.e., the decoder continuous the decoding process at the last message state.\n",
    "\n",
    "\n",
    "The BP decoder itself does not have any trainable weights. However, the LDPCBPDecoder API allows to register custom callback functions after each VN/CN node update step. In this tutorial, we use the `WeightedBPCallback` to apply trainable weights to each exchanged internal decoder message. Similarly, offset-corrected BP can be made trainable."
   ]
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    "class WeightedBP(Block):\n",
    "    \"\"\"System model for BER simulations of weighted BP decoding.\n",
    "\n",
    "    This model uses `GaussianPriorSource` to mimic the LLRs after demapping of\n",
    "    QPSK symbols transmitted over an AWGN channel.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    pcm: ndarray\n",
    "        The parity-check matrix of the code under investigation.\n",
    "\n",
    "    num_iter: int\n",
    "        Number of BP decoding iterations.\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "    batch_size: int or tf.int\n",
    "        The batch_size used for the simulation.\n",
    "\n",
    "    ebno_db: float or tf.float\n",
    "        A float defining the simulation SNR.\n",
    "\n",
    "    Output\n",
    "    ------\n",
    "    (u, u_hat, loss):\n",
    "        Tuple:\n",
    "\n",
    "    u: tf.float32\n",
    "        A tensor of shape `[batch_size, k] of 0s and 1s containing the transmitted information bits.\n",
    "\n",
    "    u_hat: tf.float32\n",
    "        A tensor of shape `[batch_size, k] of 0s and 1s containing the estimated information bits.\n",
    "\n",
    "    loss: tf.float32\n",
    "        Binary cross-entropy loss between `u` and `u_hat`.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, pcm, num_iter=5):\n",
    "        super().__init__()\n",
    "\n",
    "        # add trainable weights via decoder callbacks\n",
    "        self.edge_weights = WeightedBPCallback(num_edges=np.sum(pcm))\n",
    "\n",
    "        # init components\n",
    "        self.decoder = LDPCBPDecoder(pcm,\n",
    "                                     num_iter=1, # iterations are done via outer loop (to access intermediate results for multi-loss)\n",
    "                                     return_state=True, # decoder stores internal messages after call\n",
    "                                     hard_out=False, # we need to access soft-information\n",
    "                                     cn_update=\"boxplus\",\n",
    "                                     v2c_callbacks=[self.edge_weights])# register callback to make the decoder trainable\n",
    "\n",
    "        # used to generate llrs during training (see example notebook on all-zero codeword trick)\n",
    "        self.llr_source = GaussianPriorSource()\n",
    "        self._num_iter = num_iter\n",
    "\n",
    "        self._bce = BinaryCrossentropy(from_logits=True)\n",
    "\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        noise_var = ebnodb2no(ebno_db,\n",
    "                              num_bits_per_symbol=2, # QPSK\n",
    "                              coderate=self.decoder.coderate)\n",
    "\n",
    "        # all-zero CW to calculate loss / BER\n",
    "        c = tf.zeros([batch_size, self.decoder.n])\n",
    "\n",
    "        # Gaussian LLR source\n",
    "        llr = self.llr_source([batch_size, self.decoder.n], noise_var)\n",
    "\n",
    "        # --- implement multi-loss as proposed by Nachmani et al. [1]---\n",
    "        loss = 0\n",
    "        msg_v2c = None # internal state of decoder\n",
    "        for i in range(self._num_iter):\n",
    "            c_hat, msg_v2c = self.decoder(llr, msg_v2c=msg_v2c) # perform one decoding iteration; decoder returns soft-values\n",
    "            loss += self._bce(c, c_hat)  # add loss after each iteration\n",
    "\n",
    "        loss /= self._num_iter # scale loss by number of iterations\n",
    "\n",
    "        return c, c_hat, loss"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a parity-check matrix used for the experiment. We use the same BCH(63,45) code as in [1].\n",
    "The code can be replaced by any parity-check matrix of your choice. "
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "n: 63, k: 45, coderate: 0.714\n"
     ]
    }
   ],
   "source": [
    "pcm_id = 1 # (63,45) BCH code parity check matrix\n",
    "pcm, k , n, coderate = load_parity_check_examples(pcm_id=pcm_id, verbose=True)\n",
    "\n",
    "num_iter = 10 # set number of decoding iterations\n",
    "\n",
    "# and initialize the model\n",
    "model = WeightedBP(pcm=pcm, num_iter=num_iter)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: weighted BP tends to work better for small number of iterations.\n",
    "The effective gains (compared to the baseline with same number of iterations)\n",
    "vanish with more iterations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weights *before* Training and Simulation of BER\n",
    "\n",
    "Let us plot the weights after initialization of the decoder to verify that everything is properly initialized.\n",
    "This is equivalent the *classical* BP decoder."
   ]
  },
  {
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   "execution_count": 4,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of weights:  432\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count number of weights/edges\n",
    "print(\"Total number of weights: \", np.size(model.edge_weights.weights))\n",
    "\n",
    "# and show the weight distribution\n",
    "model.edge_weights.show_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first simulate (and store) the BER performance *before* training.\n",
    "For this, we use the `PlotBER` class, which provides a convenient way to store the results for later comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T01:44:13.201541Z",
     "iopub.status.busy": "2025-03-11T01:44:13.201142Z",
     "iopub.status.idle": "2025-03-11T01:44:25.967678Z",
     "shell.execute_reply": "2025-03-11T01:44:25.967039Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      1.0 | 8.6508e-02 | 9.6500e-01 |        5450 |       63000 |          965 |        1000 |         6.6 |reached target bit errors\n",
      "      1.5 | 7.4159e-02 | 9.2100e-01 |        4672 |       63000 |          921 |        1000 |         0.0 |reached target bit errors\n",
      "      2.0 | 5.9968e-02 | 8.0400e-01 |        3778 |       63000 |          804 |        1000 |         0.0 |reached target bit errors\n",
      "      2.5 | 4.6937e-02 | 6.8300e-01 |        2957 |       63000 |          683 |        1000 |         0.0 |reached target bit errors\n",
      "      3.0 | 3.2381e-02 | 4.8800e-01 |        2040 |       63000 |          488 |        1000 |         0.0 |reached target bit errors\n",
      "      3.5 | 2.1563e-02 | 3.5450e-01 |        2717 |      126000 |          709 |        2000 |         0.1 |reached target bit errors\n",
      "      4.0 | 1.3878e-02 | 2.4100e-01 |        2623 |      189000 |          723 |        3000 |         0.1 |reached target bit errors\n",
      "      4.5 | 8.2103e-03 | 1.4550e-01 |        2069 |      252000 |          582 |        4000 |         0.1 |reached target bit errors\n",
      "      5.0 | 3.8342e-03 | 7.3222e-02 |        2174 |      567000 |          659 |        9000 |         0.3 |reached target bit errors\n",
      "      5.5 | 2.3333e-03 | 4.3643e-02 |        2058 |      882000 |          611 |       14000 |         0.5 |reached target bit errors\n",
      "      6.0 | 1.0389e-03 | 1.9677e-02 |        2029 |     1953000 |          610 |       31000 |         1.1 |reached target bit errors\n",
      "      6.5 | 4.5465e-04 | 9.2714e-03 |        2005 |     4410000 |          649 |       70000 |         2.5 |reached target bit errors\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SNR to simulate the results\n",
    "ebno_dbs = np.array(np.arange(1, 7, 0.5))\n",
    "mc_iters = 100 # number of Monte Carlo iterations\n",
    "\n",
    "# we generate a new PlotBER() object to simulate, store and plot the BER results\n",
    "ber_plot = PlotBER(\"Weighted BP\")\n",
    "\n",
    "# simulate and plot the BER curve of the untrained decoder\n",
    "ber_plot.simulate(model,\n",
    "                  ebno_dbs=ebno_dbs,\n",
    "                  batch_size=1000,\n",
    "                  num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                  legend=\"Untrained\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=mc_iters,\n",
    "                  forward_keyboard_interrupt=False,\n",
    "                  graph_mode=\"graph\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\n",
    "We now train the model for a fixed number of SGD training iterations.\n",
    "\n",
    "**Note**: this is a very basic implementation of the training loop.\n",
    "You can also try more sophisticated training loops with early stopping, different hyper-parameters or optimizers etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T01:44:25.972858Z",
     "iopub.status.busy": "2025-03-11T01:44:25.972662Z",
     "iopub.status.idle": "2025-03-11T01:44:46.362306Z",
     "shell.execute_reply": "2025-03-11T01:44:46.361215Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.049836 ber: 0.0132 bmi: 0.939\n",
      "Current loss: 0.050902 ber: 0.0129 bmi: 0.943\n",
      "Current loss: 0.047043 ber: 0.0129 bmi: 0.945\n",
      "Current loss: 0.041731 ber: 0.0117 bmi: 0.948\n",
      "Current loss: 0.043182 ber: 0.0131 bmi: 0.948\n",
      "Current loss: 0.037416 ber: 0.0116 bmi: 0.955\n",
      "Current loss: 0.039514 ber: 0.0126 bmi: 0.951\n",
      "Current loss: 0.038904 ber: 0.0123 bmi: 0.949\n",
      "Current loss: 0.038560 ber: 0.0118 bmi: 0.951\n",
      "Current loss: 0.043944 ber: 0.0137 bmi: 0.944\n",
      "Current loss: 0.039379 ber: 0.0124 bmi: 0.950\n",
      "Current loss: 0.040292 ber: 0.0136 bmi: 0.948\n",
      "Current loss: 0.040030 ber: 0.0132 bmi: 0.950\n",
      "Current loss: 0.039854 ber: 0.0124 bmi: 0.950\n",
      "Current loss: 0.039948 ber: 0.0132 bmi: 0.951\n",
      "Current loss: 0.038435 ber: 0.0119 bmi: 0.956\n",
      "Current loss: 0.042012 ber: 0.0128 bmi: 0.949\n",
      "Current loss: 0.042593 ber: 0.0127 bmi: 0.947\n",
      "Current loss: 0.043055 ber: 0.0137 bmi: 0.946\n",
      "Current loss: 0.036739 ber: 0.0121 bmi: 0.954\n"
     ]
    }
   ],
   "source": [
    "# training parameters\n",
    "batch_size = 1000\n",
    "train_iter = 200\n",
    "ebno_db = 4.0\n",
    "clip_value_grad = 10 # gradient clipping for stable training convergence\n",
    "\n",
    "# try also different optimizers or different hyperparameters\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)\n",
    "\n",
    "@tf.function\n",
    "def train_step():\n",
    "    with tf.GradientTape() as tape:\n",
    "        b, llr, loss = model(batch_size, ebno_db)\n",
    "    grads = tape.gradient(loss, tape.watched_variables())\n",
    "    grads = [tf.clip_by_value(g, -clip_value_grad, clip_value_grad, name=None) for g in grads]\n",
    "    optimizer.apply_gradients(zip(grads, tape.watched_variables()))\n",
    "    return loss, b, llr\n",
    "\n",
    "for it in range(0, train_iter):\n",
    "    loss, b, llr = train_step()\n",
    "\n",
    "    # calculate and print intermediate metrics\n",
    "    # only for information\n",
    "    # this has no impact on the training\n",
    "    if it%10==0: # evaluate every 10 iterations\n",
    "        # calculate ber from received LLRs\n",
    "        b_hat = hard_decisions(llr) # hard decided LLRs first\n",
    "        ber = compute_ber(b, b_hat)\n",
    "        # and print results\n",
    "        mi = llr2mi(llr, -2*b+1).numpy() # calculate bit-wise mutual information\n",
    "        l = loss.numpy() # copy loss to numpy for printing\n",
    "        print(f\"Current loss: {l:3f} ber: {ber:.4f} bmi: {mi:.3f}\".format())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "After training, the weights of the decoder have changed.\n",
    "In average, the weights are smaller after training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T01:44:46.365998Z",
     "iopub.status.busy": "2025-03-11T01:44:46.365587Z",
     "iopub.status.idle": "2025-03-11T01:44:46.546286Z",
     "shell.execute_reply": "2025-03-11T01:44:46.545637Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.edge_weights.show_weights() # show weights AFTER training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let us compare the new BER performance.\n",
    "For this, we can simply call the ber_plot.simulate() function again as it internally stores all previous results (if `add_results` is True)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T01:44:46.549766Z",
     "iopub.status.busy": "2025-03-11T01:44:46.549548Z",
     "iopub.status.idle": "2025-03-11T01:44:58.174525Z",
     "shell.execute_reply": "2025-03-11T01:44:58.172944Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      1.0 | 8.9317e-02 | 9.9500e-01 |        5627 |       63000 |          995 |        1000 |         3.2 |reached target bit errors\n",
      "      1.5 | 7.7492e-02 | 9.8000e-01 |        4882 |       63000 |          980 |        1000 |         0.0 |reached target bit errors\n",
      "      2.0 | 6.3794e-02 | 9.3400e-01 |        4019 |       63000 |          934 |        1000 |         0.0 |reached target bit errors\n",
      "      2.5 | 5.0635e-02 | 8.3500e-01 |        3190 |       63000 |          835 |        1000 |         0.0 |reached target bit errors\n",
      "      3.0 | 3.3794e-02 | 6.0000e-01 |        2129 |       63000 |          600 |        1000 |         0.0 |reached target bit errors\n",
      "      3.5 | 2.1627e-02 | 4.1150e-01 |        2725 |      126000 |          823 |        2000 |         0.1 |reached target bit errors\n",
      "      4.0 | 1.2545e-02 | 2.5067e-01 |        2371 |      189000 |          752 |        3000 |         0.1 |reached target bit errors\n",
      "      4.5 | 6.2275e-03 | 1.2900e-01 |        2354 |      378000 |          774 |        6000 |         0.2 |reached target bit errors\n",
      "      5.0 | 3.1921e-03 | 6.7100e-02 |        2011 |      630000 |          671 |       10000 |         0.4 |reached target bit errors\n",
      "      5.5 | 1.2540e-03 | 2.9500e-02 |        2054 |     1638000 |          767 |       26000 |         0.9 |reached target bit errors\n",
      "      6.0 | 4.7642e-04 | 1.2662e-02 |        2041 |     4284000 |          861 |       68000 |         2.4 |reached target bit errors\n",
      "      6.5 | 2.1810e-04 | 5.8800e-03 |        1374 |     6300000 |          588 |      100000 |         3.6 |reached max iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ebno_dbs = np.array(np.arange(1, 7, 0.5))\n",
    "batch_size = 10000\n",
    "mc_ites = 100\n",
    "\n",
    "ber_plot.simulate(model,\n",
    "                  ebno_dbs=ebno_dbs,\n",
    "                  batch_size=1000,\n",
    "                  num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                  legend=\"Trained\",\n",
    "                  max_mc_iter=mc_iters,\n",
    "                  soft_estimates=True,\n",
    "                  graph_mode=\"graph\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Experiments\n",
    "\n",
    "You will now see that the memory footprint can be drastically reduced by using the same weight for all messages.\n",
    "In the second part we will apply the concept to the 5G LDPC codes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Damped BP \n",
    "\n",
    "It is well-known that scaling of LLRs / messages can help to improve the performance of BP decoding in some scenarios [3,4].\n",
    "In particular, this works well for very short codes such as the code we are currently analyzing.\n",
    "\n",
    "We now follow the basic idea of [2] and scale all weights with the same scalar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T01:44:58.178927Z",
     "iopub.status.busy": "2025-03-11T01:44:58.178283Z",
     "iopub.status.idle": "2025-03-11T01:45:09.973776Z",
     "shell.execute_reply": "2025-03-11T01:45:09.973136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      1.0 | 8.8540e-02 | 9.9400e-01 |        5578 |       63000 |          994 |        1000 |         3.2 |reached target bit errors\n",
      "      1.5 | 7.6889e-02 | 9.7700e-01 |        4844 |       63000 |          977 |        1000 |         0.0 |reached target bit errors\n",
      "      2.0 | 6.2968e-02 | 9.3200e-01 |        3967 |       63000 |          932 |        1000 |         0.0 |reached target bit errors\n",
      "      2.5 | 4.7063e-02 | 7.7600e-01 |        2965 |       63000 |          776 |        1000 |         0.0 |reached target bit errors\n",
      "      3.0 | 3.4413e-02 | 6.1400e-01 |        2168 |       63000 |          614 |        1000 |         0.0 |reached target bit errors\n",
      "      3.5 | 2.2960e-02 | 4.1950e-01 |        2893 |      126000 |          839 |        2000 |         0.1 |reached target bit errors\n",
      "      4.0 | 1.2392e-02 | 2.4133e-01 |        2342 |      189000 |          724 |        3000 |         0.1 |reached target bit errors\n",
      "      4.5 | 5.8862e-03 | 1.2183e-01 |        2225 |      378000 |          731 |        6000 |         0.2 |reached target bit errors\n",
      "      5.0 | 2.8320e-03 | 6.0333e-02 |        2141 |      756000 |          724 |       12000 |         0.4 |reached target bit errors\n",
      "      5.5 | 1.2990e-03 | 2.9760e-02 |        2046 |     1575000 |          744 |       25000 |         0.9 |reached target bit errors\n",
      "      6.0 | 5.1146e-04 | 1.3111e-02 |        2030 |     3969000 |          826 |       63000 |         2.2 |reached target bit errors\n",
      "      6.5 | 2.2333e-04 | 6.1000e-03 |        1407 |     6300000 |          610 |      100000 |         3.5 |reached max iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get weights of trained model\n",
    "weights_bp = model.edge_weights.weights\n",
    "\n",
    "# calc mean value of weights\n",
    "damping_factor = tf.reduce_mean(weights_bp)\n",
    "\n",
    "# set all weights to the SAME constant scaling\n",
    "weights_damped = tf.ones_like(weights_bp) * damping_factor\n",
    "\n",
    "# and apply the new weights\n",
    "model.edge_weights.weights.assign(weights_damped)\n",
    "\n",
    "# let us have look at the new weights again\n",
    "model.edge_weights.show_weights()\n",
    "\n",
    "# and simulate the BER again\n",
    "leg_str = f\"Damped BP (scaling factor {damping_factor.numpy():.3f})\"\n",
    "ber_plot.simulate(model,\n",
    "                  ebno_dbs=ebno_dbs,\n",
    "                  batch_size=1000,\n",
    "                  num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                  legend=leg_str,\n",
    "                  max_mc_iter=mc_iters,\n",
    "                  soft_estimates=True,\n",
    "                  graph_mode=\"graph\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the results, we observe almost the same performance although we only scale by a single scalar.\n",
    "This implies that the number of weights of our model is by far too large and the memory footprint could be reduced significantly.\n",
    "However, isn't it fascinating to see that this simple concept of weighted BP leads to the same results as the concept of *damped BP*?\n",
    "\n",
    "**Note**: for more iterations it could be beneficial to implement an individual damping per iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Learning the 5G LDPC Code\n",
    "\n",
    "In this Section, you will experience what happens if we apply the same concept to the 5G LDPC code (including rate matching).\n",
    "\n",
    "For this, we need to define a new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T01:45:09.980167Z",
     "iopub.status.busy": "2025-03-11T01:45:09.979848Z",
     "iopub.status.idle": "2025-03-11T01:45:09.988002Z",
     "shell.execute_reply": "2025-03-11T01:45:09.987070Z"
    }
   },
   "outputs": [],
   "source": [
    "class WeightedBP5G(Block):\n",
    "    \"\"\"System model for BER simulations of weighted BP decoding for 5G LDPC codes.\n",
    "\n",
    "    This model uses `GaussianPriorSource` to mimic the LLRs after demapping of\n",
    "    QPSK symbols transmitted over an AWGN channel.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    k: int\n",
    "        Number of information bits per codeword.\n",
    "\n",
    "    n: int\n",
    "        Codeword length.\n",
    "\n",
    "    num_iter: int\n",
    "        Number of BP decoding iterations.\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "    batch_size: int or tf.int\n",
    "        The batch_size used for the simulation.\n",
    "\n",
    "    ebno_db: float or tf.float\n",
    "        A float defining the simulation SNR.\n",
    "\n",
    "    Output\n",
    "    ------\n",
    "    (u, u_hat, loss):\n",
    "        Tuple:\n",
    "\n",
    "    u: tf.float32\n",
    "        A tensor of shape `[batch_size, k] of 0s and 1s containing the transmitted information bits.\n",
    "\n",
    "    u_hat: tf.float32\n",
    "        A tensor of shape `[batch_size, k] of 0s and 1s containing the estimated information bits.\n",
    "\n",
    "    loss: tf.float32\n",
    "        Binary cross-entropy loss between `u` and `u_hat`.\n",
    "    \"\"\"\n",
    "    def __init__(self, k, n, num_iter=20):\n",
    "        super().__init__()\n",
    "\n",
    "        # we need to initialize an encoder for the 5G parameters\n",
    "        self.encoder = LDPC5GEncoder(k, n)\n",
    "\n",
    "        # add trainable weights via decoder callbacks\n",
    "        self.edge_weights = WeightedBPCallback(\n",
    "                            num_edges=int(np.sum(self.encoder.pcm)))\n",
    "\n",
    "        self.decoder = LDPC5GDecoder(self.encoder,\n",
    "                                     num_iter=1, # iterations are done via outer loop (to access intermediate results for multi-loss)\n",
    "                                     return_state=True,\n",
    "                                     hard_out=False,\n",
    "                                     prune_pcm=False,\n",
    "                                     cn_update=\"boxplus\",\n",
    "                                     v2c_callbacks=[self.edge_weights,]) # register callback\n",
    "\n",
    "        self.llr_source = GaussianPriorSource()\n",
    "        self._num_iter = num_iter\n",
    "        self._coderate = k/n\n",
    "\n",
    "        self._bce = BinaryCrossentropy(from_logits=True)\n",
    "    def call(self, batch_size, ebno_db):\n",
    "\n",
    "        noise_var = ebnodb2no(ebno_db,\n",
    "                              num_bits_per_symbol=2, # QPSK\n",
    "                              coderate=self._coderate)\n",
    "\n",
    "        # BPSK modulated all-zero CW\n",
    "        c = tf.zeros([batch_size, k]) # decoder only returns info bits\n",
    "\n",
    "        # use fake llrs from GA\n",
    "        # works as BP is symmetric\n",
    "        llr = self.llr_source([batch_size, n], noise_var)\n",
    "\n",
    "        # --- implement multi-loss is proposed by Nachmani et al. ---\n",
    "        loss = 0\n",
    "        msg_v2c = None\n",
    "        for i in range(self._num_iter):\n",
    "            c_hat, msg_v2c = self.decoder(llr, msg_v2c=msg_v2c) # perform one decoding iteration; decoder returns soft-values\n",
    "            loss += self._bce(c, c_hat)  # add loss after each iteration\n",
    "\n",
    "        return c, c_hat, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T01:45:09.990694Z",
     "iopub.status.busy": "2025-03-11T01:45:09.990413Z",
     "iopub.status.idle": "2025-03-11T01:46:02.309330Z",
     "shell.execute_reply": "2025-03-11T01:46:02.308670Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.6887e-01 | 1.0000e+00 |       67548 |      400000 |         1000 |        1000 |         3.9 |reached target bit errors\n",
      "     0.25 | 1.4882e-01 | 1.0000e+00 |       59528 |      400000 |         1000 |        1000 |         0.1 |reached target bit errors\n",
      "      0.5 | 1.2150e-01 | 9.9800e-01 |       48600 |      400000 |          998 |        1000 |         0.1 |reached target bit errors\n",
      "     0.75 | 9.7977e-02 | 9.8300e-01 |       39191 |      400000 |          983 |        1000 |         0.1 |reached target bit errors\n",
      "      1.0 | 6.5448e-02 | 9.3500e-01 |       26179 |      400000 |          935 |        1000 |         0.1 |reached target bit errors\n",
      "     1.25 | 4.2583e-02 | 8.2000e-01 |       17033 |      400000 |          820 |        1000 |         0.1 |reached target bit errors\n",
      "      1.5 | 2.2090e-02 | 6.3100e-01 |        8836 |      400000 |          631 |        1000 |         0.1 |reached target bit errors\n",
      "     1.75 | 9.1425e-03 | 3.7800e-01 |        3657 |      400000 |          378 |        1000 |         0.1 |reached target bit errors\n",
      "      2.0 | 2.7050e-03 | 1.7850e-01 |        2164 |      800000 |          357 |        2000 |         0.2 |reached target bit errors\n",
      "     2.25 | 8.3750e-04 | 7.1000e-02 |        2010 |     2400000 |          426 |        6000 |         0.5 |reached target bit errors\n",
      "      2.5 | 2.3580e-04 | 2.1773e-02 |        2075 |     8800000 |          479 |       22000 |         2.0 |reached target bit errors\n",
      "     2.75 | 3.7100e-05 | 5.0700e-03 |        1484 |    40000000 |          507 |      100000 |         8.8 |reached max iterations\n",
      "      3.0 | 3.9750e-06 | 8.7000e-04 |         159 |    40000000 |           87 |      100000 |         8.9 |reached max iterations\n",
      "     3.25 | 5.2500e-07 | 1.4000e-04 |          21 |    40000000 |           14 |      100000 |         8.9 |reached max iterations\n",
      "      3.5 | 3.0000e-07 | 5.0000e-05 |          12 |    40000000 |            5 |      100000 |         8.8 |reached max iterations\n",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    40000000 |            0 |      100000 |         8.8 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 3.8 dB.\n",
      "\n"
     ]
    },
    {
     "data": {
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ervYdO3asbVeqVZf+FRcXG3379nW19/X1Nf71r38Z+fn5rjZ2u92YPXu2ERYWVuH7PjMzs8pznjiyNCQkxJBk/PWvfzWysrIqtN21a1e9+3nidW644QajU6dORmBgoBEYGGi0adPGOO+884x//etfRnp6eq3O+c9//rPCObdt21bv+BpaYxvBWlRUVKuRpHUdwZqZmVnh8+2WW26psP/OO++s8P1os9nqHDsAAKcbc7ACAIBKyo8oTU9Pr3IU2sqVK10LYI0YMcK1vfzj6uZhLb+9/KJaZWbNmuVa+CQiIkJr1qzRPffcU2nEY0REhP773//queeec2178MEHT2nF68cff1wFBQWSpLCwMC1ZskQXXnhhhbkkg4KC9OSTT+rVV19VYWGh8vLy6nSNw4cPKzo6WqtWrdIll1wiL68/fiTz8fHRww8/rHvuuce1bfbs2fXuT3VuueUWV9wXXXSRVq5cqVGjRlWaM3Pw4MFasWKF+vbtK0lKS0vTq6++WuU5p02b5lrh3t/fXz/88IMmTZpUoX/NmjXTK6+8oieeeKJer11DcDgcysjI0MKFC3XhhRfqtddec+17+OGH1aNHjwa71tatWyuMjh0yZEiDnbs23njjDf3666+u57Nnz9ZDDz2kwMBA1zYfHx/deOONWrhwoWs0YXp6up588slaXSM3N1fPP/+8/vOf/6h58+YV9nXs2PHUO3Hc+++/r127dqmgoEAFBQXau3evfvjhBz388MNq166dnnzySTkcjpOeY+PGja7HwcHB6tq1a4PF52l8fX0rjOYuGyl/qubNm1fhM/rE74kNGza4Hnfp0kUhISENcl0AAMxEgRUAAFQyfPjwCquTV1UoPXGBqzJxcXGuBWNWrFhR6bi8vDxt2rTJ9fzE6QEMw9D06dNdz//zn//UWPB67LHHXG127dqlRYsWnbR9dbKzsyuslv3UU0+pc+fO1ba/++67dfbZZ9frWi+//LLatm1b7f7yK2jv3btXe/furdd1qvLzzz+73r/mzZtrzpw5FRadOVFQUJDefPNN1/M333yzUhG7oKBA77//vuv5/fffrwEDBlR7zqeffrrCwlJmqWoRKB8fH8XExOjCCy/UwoULJUmtW7fW66+/3qCL+eTl5emWW26p8FpdccUVDXb+mhiGoddff931/LLLLtM111xTbfthw4ZVyLv333//pAutlenTp48eeeSRU4q1Nlq0aKGhQ4dqzJgx6tevn/z9/V37CgsL9dxzz+nCCy886YJsmZmZFc5XvviPyiIjI12Py6YuORVJSUkVCvd+fn7605/+VKFN+ffoZJ+RAAA0JvxEAQAAKgkJCVG/fv1cz09WYPX29q40J+c555wjSdq/f3+lUU+rV6+uMD/niSNYN27cqMTERElSdHR0rVaAtlgsFdr99NNPNR5TlcWLF7vm/rNarbr55ptrPOaOO+6o83VCQ0NPWuiSpA4dOqhly5au59u3b6/zdapTvhA6ZcoUNWvWrMZjhgwZok6dOkmSDhw4UCmeJUuWyGazSSp9P+6+++6Tns/Hx6der50ZYmNjdeedd2rSpEmnfC6Hw6H9+/drzpw5GjhwoH7++WfXvkGDBumyyy475WvU1vbt2yuMPv/rX/9a4zF/+ctfXEXH/Px8/fjjjzUec8stt5hSqLRYLBo8eLDefvttHThwQAcOHNDq1au1ePFi/fLLLzp69Kjmzp2ruLg41zE//PCD/vKXv1R7zrIR1lLpCPXaeOKJJ3T++eef9Ov333+vdz8bs/DwcNfj8q9dXRQVFWnHjh168cUXNWTIkArnuffee9WiRYsK7cvmA5Zq/x4BAOBuLHIFAACqNHLkSNetmicWWI8dO+ba17dv30q3cI4YMUIff/yx69jytwmXP1d0dLS6d+9e4djyo15HjhxZYSTtyZS/lfWXX36p1TEnWr9+vetx3759KxQXqlPbhYDKGzBgwElHjJZp1aqVa2Gio0eP1vk61Sn/Go8ZM6bWx/Xq1Uu7du2SVPoal3/vyr92PXr0cI1iPpmJEyfqb3/7W62vXx9VLQJlGIby8/OVnJysgwcP6tChQ3riiSf0r3/9S2+99VaNxe/yTpxSoTpxcXH68ssva92+IZQv7gYFBVUYaV6dVq1aqV+/fq5b6X/++ecaR92W/UGlobVr165CH07k5+ena6+9Vueff77Gjx/vinnGjBm666671KdPn0rHlF88yc/Pr1ZxbNiwocLCYlU5HSN43aFsGhipdrm+bNmyWuf4xIkT9cILL1TaXv49qu+icAAAnG4UWAEAQJVGjhypf//735KkPXv2KC0tTe3atZMkrV271nUbblVzqJ44D2v5kaAnFlBPtGXLFtfj9evX6/zzz69VvOVHRWVlZdXqmBPt2bPH9bhbt261OiYuLk7+/v4qLCys9XViY2Nr1a78PJll88KeKsMwKswJ+s9//lP/+9//anXs5s2bXY9PfI3LCq9SxWL3yXTp0kVWq/Wkt3SfqtmzZ1cY4XiiLVu26PHHH9f8+fOVm5ur6667TlartcFu5ffz89MNN9yg6dOn12qkcEMq/5707Nmz1qNMe/fu7SpWlj9HdRpyntX6CA8P1xdffKGuXbuqsLBQhmHotdde01tvvVWpbfn3oGzENapXfoqIiIiIBjlnZGSk/v73v+vBBx+sMiebNWvmmiaA9wgA0FRQYAUAAFUaMWKELBaLa/7I5cuX64YbbnA9Lt/uRL169VKzZs109OjRCm2Lioq0bt061/OqCqzlbw9NS0tTWlpanWOvzbyRVSk/SrQuxbCwsLA6FVjrMyrrVBbuKi8nJ6fCQkCrV6+u93nKKz8/44kLHVXH29tbYWFh9S6IN4RevXrpq6++0iWXXKIFCxbI6XTqjjvu0IQJE2q1uM55551X4bmPj49CQ0MVGxurQYMGafz48RXmsTydyudzbd8Tqe7zbjaGRYjatm2ra665RrNmzZIkJSQkVNmufJGwtnOKfv/995W2paamqn379nUPtIkpPx9qbQqs4eHhGjx4cIVtfn5+CgsLU1xcnM4++2yNHTv2pKOHIyIiXNdtiHlfAQA4HSiwAgCAKkVERKhXr16uUYvVFViruj3YYrFo+PDh+vbbb5WcnKz9+/erVatWWrduXYVC5IkLXEml8z6eqvK3tZ4OTWmhnIZ4faXKr3FxcbHrcV0KyLW9TdtMFotF06ZN04IFCySVFvk/+eQT3XrrrTUeW1XxrbGo763W5duWP0d1Gkv+jx492lVgTU1NVXFxcaV+lx/NvH//fuXk5DDPZzWKiooq3FFQm5HKffr0OeXviQ4dOrjmDi4/2h4AgMascfw0BAAAGqXyBdCyoqrdbtfatWslld5GHxUVVeWx5QuvZceWL8xGRESod+/elY4rX+yYOnWqDMOo81dqamq9+lv+2nUZBduQ86Oa7cRi0vr16+v1Gj/zzDMVzhMaGup6nJubW+t46tLWTH379lVQUJDr+cqVK90YTcMo/17X9z053dManIqTLZZUpvznkmEYrrmkUdmGDRsq/OGkNnP4NoTy79GRI0eUlJR0Wq4LAMCpoMAKAACqVf4W/qSkJKWnp2vDhg2u+UBP9gv3ifOwlv9XKv0luqrFUGJiYlyPMzIy6h98PbRt29b1ePv27bU6Ji0tTceOHTMrpAYXHBxcYW7XhnqNo6OjXY9rW+DOzs5uNHMsWiyWCgXJgwcPujGahlH+jx8pKSm1Pm737t1VnqOxO3Ge4vJ5XubEUfOffvqpqTE1Ze+8806F51VN6WKGExcO/OSTT07LdQEAOBUUWAEAQLVO/IV6+fLlNc6/WmbQoEHy9/d3HedwOCrM91nV9ACSdPbZZ7sel42UPV0GDhzoevzrr7/WamTqsmXLTIyoZuVvz67tPK1mvMb9+vVzPf7ll18qzPNanfLz8bqbYRgV3u+AgAD3BdNA+vfv73qcmppaq2K6w+GoMKqz/Dkau23btrkel837eaI2bdpo/Pjxrudz585tUiPQT5ddu3bp448/dj0fMGBAhc9HMw0dOlQ9e/Z0PX/nnXdqNVUFAADuRIEVAABUKyYmRl27dnU9P7HAerIRTb6+vho0aJAkKTExUYsWLVJeXl6Nx44dO1be3t6SpL1792rJkiWn1Ie6GDt2rGvOxuLiYtd8jiczY8YMk6M6ufK3tdd2JG35hZk+/PDDWhVDa1K+2H7kyJFqFxkqr3wBx902btxYYQRku3bt3BhNwxg8eLArnw3DqNVIwISEhAqF2NN1W/ipOrF/Q4cOrbbtQw895Hqcm5ure++919TYmpqCggJNmjSpwnzZjz/++Gm7vsVi0YMPPuh6vmfPHj399NP1OpfT6VRycnJDhQYAQLUosAIAgJMqP9J06dKlWrVqlaTSkWA1FaHKijOGYeif//yna3tISEiFEY/ltWzZUldffbXr+V//+tdKt/6aJTIyUpdffrnr+bPPPlvhdukTzZgxo8KoXHeIjY11Pd61a1etjrn11lsVHBwsSUpOTtYLL7xwynF0795dAwYMcD1/6qmnTlq43bZtmz788MNTvm5DcDqdeuqppypsmzhxopuiaThhYWG64oorXM9feOGFk07JUFJSoscee8z1vG/fvk1mBGt8fLx+//131/NLL7202rbjxo2r8Lp88MEHeu6558wMr8nYuXOnzj77bP3666+ubVdeeeVJX08z3HDDDRXmYp0+fbpmzpxZp3NkZ2dr4sSJmj9/fkOHBwBAJRRYAQDASZUfabplyxbX4k+1GdlWvk35RYPOOecc1yjVqkybNk0hISGSpM2bN2v8+PHas2fPSa/ldDqVkJCg8847z7UCdX08//zzrtvDjxw5onPPPVffffddhdvv8/Pz9fzzz2vq1Kny9/d3FSvdoXwB7Ndff63ViN+IiAg98cQTrudPPfWUpk2bJrvdftLjjh49qldffbVCAby8Rx55xPV4/fr1uvvuu1VSUlKp3b59+3TppZdWue9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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate model\n",
    "num_iter = 10\n",
    "k = 400\n",
    "n = 800\n",
    "\n",
    "model5G = WeightedBP5G(k, n, num_iter=num_iter)\n",
    "\n",
    "# generate baseline BER\n",
    "ebno_dbs = np.array(np.arange(0, 4, 0.25))\n",
    "mc_iters = 100 # number of monte carlo iterations\n",
    "ber_plot_5G = PlotBER(\"Weighted BP for 5G LDPC\")\n",
    "\n",
    "# simulate the untrained performance\n",
    "ber_plot_5G.simulate(model5G,\n",
    "                     ebno_dbs=ebno_dbs,\n",
    "                     batch_size=1000,\n",
    "                     num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                     legend=\"Untrained\",\n",
    "                     soft_estimates=True,\n",
    "                     max_mc_iter=mc_iters,\n",
    "                     graph_mode=\"graph\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's train this new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T01:46:02.314693Z",
     "iopub.status.busy": "2025-03-11T01:46:02.314292Z",
     "iopub.status.idle": "2025-03-11T01:46:51.018315Z",
     "shell.execute_reply": "2025-03-11T01:46:51.016975Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 1.717313 ber: 0.0215 bmi: 0.921\n",
      "Current loss: 1.717737 ber: 0.0208 bmi: 0.924\n",
      "Current loss: 1.692114 ber: 0.0204 bmi: 0.925\n",
      "Current loss: 1.733131 ber: 0.0220 bmi: 0.919\n",
      "Current loss: 1.733311 ber: 0.0219 bmi: 0.920\n",
      "Current loss: 1.708051 ber: 0.0205 bmi: 0.924\n",
      "Current loss: 1.730341 ber: 0.0222 bmi: 0.918\n",
      "Current loss: 1.738736 ber: 0.0218 bmi: 0.919\n",
      "Current loss: 1.713310 ber: 0.0206 bmi: 0.923\n",
      "Current loss: 1.708243 ber: 0.0213 bmi: 0.923\n",
      "Current loss: 1.683718 ber: 0.0202 bmi: 0.925\n",
      "Current loss: 1.704449 ber: 0.0206 bmi: 0.923\n",
      "Current loss: 1.730718 ber: 0.0215 bmi: 0.921\n",
      "Current loss: 1.704801 ber: 0.0209 bmi: 0.924\n",
      "Current loss: 1.716135 ber: 0.0216 bmi: 0.921\n",
      "Current loss: 1.744884 ber: 0.0224 bmi: 0.918\n",
      "Current loss: 1.722892 ber: 0.0213 bmi: 0.922\n",
      "Current loss: 1.711179 ber: 0.0209 bmi: 0.923\n",
      "Current loss: 1.711154 ber: 0.0215 bmi: 0.921\n",
      "Current loss: 1.732857 ber: 0.0212 bmi: 0.921\n"
     ]
    }
   ],
   "source": [
    "# training parameters\n",
    "batch_size = 1000\n",
    "train_iter = 200\n",
    "clip_value_grad = 10 # gradient clipping seems to be important\n",
    "\n",
    "# smaller training SNR as the new code is longer (=stronger) than before\n",
    "ebno_db = 1.5 # rule of thumb: train at ber = 1e-2\n",
    "\n",
    "# try also different optimizers or different hyperparameters\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)\n",
    "\n",
    "@tf.function\n",
    "def train_step():\n",
    "    with tf.GradientTape() as tape:\n",
    "        b, llr, loss = model5G(batch_size, ebno_db)\n",
    "    grads = tape.gradient(loss, tape.watched_variables())\n",
    "    grads = [tf.clip_by_value(g, -clip_value_grad, clip_value_grad, name=None) for g in grads]\n",
    "    optimizer.apply_gradients(zip(grads, tape.watched_variables()))\n",
    "    return loss, b, llr\n",
    "\n",
    "# and let's go\n",
    "for it in range(0, train_iter):\n",
    "    loss, b, llr = train_step()\n",
    "\n",
    "    # calculate and print intermediate metrics\n",
    "    if it%10==0:\n",
    "        # calculate ber\n",
    "        b_hat = hard_decisions(llr)\n",
    "        ber = compute_ber(b, b_hat)\n",
    "        # and print results\n",
    "        mi = llr2mi(llr, -2*b+1).numpy() # calculate bit-wise mutual information\n",
    "        l = loss.numpy()\n",
    "        print(f\"Current loss: {l:3f} ber: {ber:.4f} bmi: {mi:.3f}\".format())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now simulate the new results and compare it to the untrained results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T01:46:51.023074Z",
     "iopub.status.busy": "2025-03-11T01:46:51.022622Z",
     "iopub.status.idle": "2025-03-11T01:47:43.772338Z",
     "shell.execute_reply": "2025-03-11T01:47:43.771701Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.6552e-01 | 1.0000e+00 |       66207 |      400000 |         1000 |        1000 |         3.6 |reached target bit errors\n",
      "     0.25 | 1.4709e-01 | 1.0000e+00 |       58835 |      400000 |         1000 |        1000 |         0.1 |reached target bit errors\n",
      "      0.5 | 1.2446e-01 | 1.0000e+00 |       49785 |      400000 |         1000 |        1000 |         0.1 |reached target bit errors\n",
      "     0.75 | 9.4812e-02 | 9.8600e-01 |       37925 |      400000 |          986 |        1000 |         0.1 |reached target bit errors\n",
      "      1.0 | 6.7075e-02 | 9.3900e-01 |       26830 |      400000 |          939 |        1000 |         0.1 |reached target bit errors\n",
      "     1.25 | 4.3540e-02 | 8.4200e-01 |       17416 |      400000 |          842 |        1000 |         0.1 |reached target bit errors\n",
      "      1.5 | 2.3047e-02 | 6.4500e-01 |        9219 |      400000 |          645 |        1000 |         0.1 |reached target bit errors\n",
      "     1.75 | 9.4325e-03 | 3.8600e-01 |        3773 |      400000 |          386 |        1000 |         0.1 |reached target bit errors\n",
      "      2.0 | 2.9487e-03 | 1.6850e-01 |        2359 |      800000 |          337 |        2000 |         0.2 |reached target bit errors\n",
      "     2.25 | 7.8071e-04 | 6.4857e-02 |        2186 |     2800000 |          454 |        7000 |         0.6 |reached target bit errors\n",
      "      2.5 | 1.6767e-04 | 1.9033e-02 |        2012 |    12000000 |          571 |       30000 |         2.7 |reached target bit errors\n",
      "     2.75 | 3.2150e-05 | 4.5300e-03 |        1286 |    40000000 |          453 |      100000 |         8.8 |reached max iterations\n",
      "      3.0 | 5.9500e-06 | 8.4000e-04 |         238 |    40000000 |           84 |      100000 |         8.8 |reached max iterations\n",
      "     3.25 | 6.2500e-07 | 1.4000e-04 |          25 |    40000000 |           14 |      100000 |         8.8 |reached max iterations\n",
      "      3.5 | 2.2500e-07 | 5.0000e-05 |           9 |    40000000 |            5 |      100000 |         8.8 |reached max iterations\n",
      "     3.75 | 1.0000e-07 | 1.0000e-05 |           4 |    40000000 |            1 |      100000 |         8.8 |reached max iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ebno_dbs = np.array(np.arange(0, 4, 0.25))\n",
    "batch_size = 1000\n",
    "mc_iters = 100\n",
    "\n",
    "ber_plot_5G.simulate(model5G,\n",
    "                     ebno_dbs=ebno_dbs,\n",
    "                     batch_size=batch_size,\n",
    "                     num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                     legend=\"Trained\",\n",
    "                     max_mc_iter=mc_iters,\n",
    "                     soft_estimates=True,\n",
    "                     graph_mode=\"graph\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, we observe only very minor gains for the 5G LDPC code. We empirically observed that gain vanishes for more iterations and longer codewords, i.e., for most practical use-cases of the 5G LDPC code the gains are only minor.\n",
    "\n",
    "However, there may be other `codes on graphs` that benefit from the principle idea of weighted BP - or other channel setups? Feel free to adjust this notebook and train for your favorite code / channel.\n",
    "\n",
    "Other ideas for own experiments:\n",
    "\n",
    "- Implement weighted BP with unique weights per iteration.\n",
    "- Apply the concept to (scaled) min-sum decoding as in [5].\n",
    "- Can you replace the complete CN update by a neural network?\n",
    "- Verify the results from all-zero simulations for a *real* system simulation with explicit encoder and random data\n",
    "- What happens in combination with higher order modulation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[1] E. Nachmani, Y. Be’ery and D. Burshtein, \"Learning to Decode Linear Codes Using Deep Learning,\"\n",
    "IEEE Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 341-346., 2016. https://arxiv.org/pdf/1607.04793.pdf\n",
    "\n",
    "[2] M. Lian, C. Häger, and H. Pfister, \"What can machine learning teach us about communications?\" IEEE Information Theory Workshop (ITW), pp. 1-5. 2018.\n",
    "\n",
    "[3] ] M. Pretti, “A message passing algorithm with damping,” J. Statist. Mech.: Theory Practice, p. 11008, Nov. 2005.\n",
    "\n",
    "[4] J.S. Yedidia, W.T. Freeman and Y. Weiss, \"Constructing free energy approximations and Generalized Belief Propagation algorithms,\" IEEE Transactions on Information Theory, 2005.\n",
    "\n",
    "[5] E. Nachmani, E. Marciano, L. Lugosch, W. Gross, D. Burshtein and Y. Be’ery, \"Deep learning methods for improved decoding of linear codes,\" IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp.119-131, 2018."
   ]
  }
 ],
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